Wednesday, December 23, 2009

Change resampling method in WarpPLS: YouTube video

The blog post below refers to resampling methods, bootstrapping and jackknifing, which are often used for the generation of estimates employed and hypothesis testing. Even though they are widely used, resampling methods are inherently unstable, as illustrated in the post. More recent versions of WarpPLS employ "stable" methods for the same purpose, with various advantages. See the most recent version of the WarpPLS User Manual (linked below) for more details.

This video shows how one can conduct an SEM analysis using WarpPLS, save that analysis with a different project name, change the resampling method (from bootstrapping to jackknifing), and then redo the analysis.

At the end, the user has two project files, one with all of the P values calculated through bootstrapping, and the other with all of the P values calculated through jackknifing.

As noted in the WarpPLS User Manual, bootstrapping and jackknifing provide a good complement to each other in the context of warped PLS-based SEM.

Thus, some users may want to run two analyses of the same model, one with each resampling method, and use the results that are associated with the most stable resample path coefficients. These will typically be the ones with the lowest P values, since P values go up as the standard errors in the resample set go up. High resample standard errors are associated with instability. The instability itself often comes from outliers, which may drastically change the shape of a warped relationship in each resample.

Well, moving from statspeach to plain English, there are good theoretical reasons to recommend that users choose the most stable results (i.e., with the lowest P values) as the results that they will use in research reports, whether they are obtained with bootstrapping or jackknifing. The choice may be made individually, for each path coefficient. This should be disclosed to the readers of the report; a sentence like this would probably be enough: "Both bootstrapping or jackknifing were used in the analyses. The results reported here are those associated with the most stable resample estimates."

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Ned Kock

About Me

I am a researcher, software developer, consultant, and college professor. Two of my main areas of research are nonlinear variance-based structural equation modeling, and evolutionary biology as it applies to the study of human-technology interaction. My degrees are in engineering (B.E.E.), computer science (M.S.), and business (Ph.D.). I am interested in the application of science, statistics, and technology to the understanding of human health and behavior. Here I blog about statistics, and more specifically about nonlinear variance-based structural equation modeling and WarpPLS, the first software to enable this type of analysis. My personal web site contains my contact information and freely available articles related to the topic of this blog: nedkock.com.

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Copyright

The contents of this blog may be used with proper attribution. Most of the issues covered here are also covered in the latest version of the WarpPLS User Manual. Therefore, you can cite the Manual to refer to issues covered here in this blog.